Projecting RNA measurements onto single cell atlases to extract cell type-specific expression profiles using scProjection

Multi-modal single cell RNA assays capture RNA content as well as other data modalities, such as spatial cell position or the electrophysiological properties of cells. Compared to dedicated scRNA-seq assays however, they may unintentionally capture RNA from multiple adjacent cells, exhibit lower RNA sequencing depth compared to scRNA-seq, or lack genome-wide RNA measurements. We present scProjection, a method for mapping individual multi-modal RNA measurements to deeply sequenced scRNA-seq atlases to extract cell type-specific, single cell gene expression profiles. We demonstrate several use cases of scProjection, including identifying spatial motifs from spatial transcriptome assays, distinguishing RNA contributions from neighboring cells in both spatial and multi-modal single cell assays, and imputing expression measurements of un-measured genes from gene markers. scProjection therefore combines the advantages of both multi-modal and scRNA-seq assays to yield precise multi-modal measurements of single cells.


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The scProjection framework was implemented in in the 'scProjection' Python package, which can be be installed through PyPI (https://pypi.org/ project/scProjection/), and the code is is available at at https://github.com/quon-titative-biology/scProjection. The data preprocessing and analysis of of results were done using R 3.6.1 and R 4. CIBERSORTx was run from their website (https://cibersortx.stanford.edu/), which did not provide versioning information at at runtime.
All data analyzed in in this article are publicly available through online sources. The gene count matrix for the RNA mixture experiments in in CellBench is is provided in in the nature portfolio | reporting summary

April 2023
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R data file that is available at https://github.com/Shians/CellBench. The gene count matrix of the bulk-RNA experiments and IHC measurements for the ROSMAP-IHC benchmark can be found at https://github.com/ellispatrick/CortexCellDeconv. Mouse Primary Motor Area (MOp) and the mouse primary visual cortex (VISp) scRNAseq datasets are from the Cell Types Database of the Allen Brain Map (https://portal.brain-map.org/atlases-and-data/rnaseq/mouse-aca-and-mop-smart-seq, and https://portal.brain-map.org/atlases-and-data/rnaseq/mouse-v1-and-alm-smart-seq, respectively). We obtained the gene count matrix for the mouse brain atlas described in Yao et al. and Tasic et al. from the Allen Institute Cell Types database: RNA-Seq data page on the Allen Institute's webpage (https://portal.brainmap.org/atlases-and-data/rnaseq We applied and benchmarked scProjection using seven publicly available, bulk-(or bulk-like) expression datasets from diverse assays, including bulk RNA sequencing, RNA imaging-based MERFISH, LCM-seq, and Patch-seq. Our results are therefore based on reasonable sample sizes.
No additional sample filtering steps were applied to the datasets obtained from the public domain.
As the entire paper is based on computational analysis, reproducibility of results was ensured by repeating our analysis scripts to ensure the same results were reproduced for each figure.
Randomization was not performed in this study. In each experiment, scProjection is trained on an entire dataset of bulk-(or bulk-like) RNA samples, and is only given a sample single cell reference dataset to help train the VAEs. Because the bulk RNA samples are not labeled in any way when input into scProjection, there was no need to define a training/testing split of the dataset.
Blinding is not relevant to this study, as there was no explicit randomization.